Differential phospho analysis - CRC Batch 2 Set 2
Sophie Herbst
Analysis date: 2023-08-08
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")TODO
Setup
Load libraries and functions
Analysis
DEP
Serine/Threonine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set2_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.61271676 0.9922314
## 2: ABC transporter disorders 0.30165289 0.9534518
## 3: ABC-family proteins mediated transport 0.18901454 0.9534518
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.01550755 0.7633058
## 5: AKT phosphorylates targets in the cytosol 0.85488959 0.9922314
## 6: AKT phosphorylates targets in the nucleus 0.36994220 0.9534518
## log2err ES NES size leadingEdge
## 1: 0.06689663 0.6847059 0.9213786 1 11168
## 2: 0.11056472 -0.8564706 -1.1474743 1 5684
## 3: 0.12563992 -0.7670509 -1.2121482 3 5684
## 4: 0.38073040 0.8537796 1.6499823 4 5577,5566,5576,5573
## 5: 0.07492788 0.3297872 0.6811472 5 2932,207,7249
## 6: 0.09374654 0.8058824 1.0844405 1 207data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.8571429 0.9912939
## 2: ABC transporter disorders 0.2118812 0.9485100
## 3: ABC-family proteins mediated transport 0.6333938 0.9620287
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.1773050 0.9485100
## 5: AKT phosphorylates targets in the cytosol 0.1428571 0.9485100
## 6: AKT phosphorylates targets in the nucleus 0.5694165 0.9620287
## log2err ES NES size leadingEdge
## 1: 0.05302125 0.5741176 0.7536820 1 11168
## 2: 0.13214726 -0.8917647 -1.1988703 1 5684
## 3: 0.06238615 -0.5093616 -0.8776526 3 5684
## 4: 0.16080140 0.6926490 1.2883339 4 5577,5576,5566,5573
## 5: 0.18470647 0.6576765 1.3356214 5 572,2932,207,7249
## 6: 0.07271411 0.7258824 0.9529136 1 207Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.61706349 0.9232387
## 2: ABC transporter disorders 0.18473896 0.8333398
## 3: ABC-family proteins mediated transport 0.55968689 0.9232387
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.04036795 0.8130633
## 5: AKT phosphorylates targets in the cytosol 0.87850467 0.9764403
## 6: AKT phosphorylates targets in the nucleus 0.74007937 0.9362964
## log2err ES NES size leadingEdge
## 1: 0.06799226 0.6894118 0.9166073 1 11168
## 2: 0.14375899 -0.9211765 -1.2184080 1 5684
## 3: 0.07217980 -0.5394246 -0.9459088 3 5684
## 4: 0.32177592 0.7725547 1.4846815 4 5577,5576,5566
## 5: 0.04850598 -0.3215130 -0.6561460 5 572,84335,2932,207,7249
## 6: 0.05922192 0.6341176 0.8430910 1 207EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.52485089 0.9721457
## 2: ABC transporter disorders 0.34990060 0.9508045
## 3: ABC-family proteins mediated transport 0.08153846 0.9508045
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.42424242 0.9508045
## 5: AKT phosphorylates targets in the cytosol 0.64855072 0.9721457
## 6: AKT phosphorylates targets in the nucleus 0.66003976 0.9721457
## log2err ES NES size leadingEdge
## 1: 0.07627972 -0.7364706 -0.9856814 1 11168
## 2: 0.09889030 -0.8235294 -1.1021997 1 5684
## 3: 0.19381330 0.8000583 1.3680596 3 8714,23,5684
## 4: 0.12043337 -0.4793388 -1.0050225 4 5566,5577,5573,5576
## 5: 0.09787733 -0.3628842 -0.8224775 5 207,2932,7249,572,84335
## 6: 0.06479434 -0.6694118 -0.8959309 1 207
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set2, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.53036437 0.9561553
## 2: ABC transporter disorders 0.86470588 0.9695444
## 3: ABC-family proteins mediated transport 0.94010889 0.9893691
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.88908766 0.9695444
## 5: AKT phosphorylates targets in the cytosol 0.02423715 0.7831726
## 6: AKT phosphorylates targets in the nucleus 0.53725490 0.9561553
## log2err ES NES size leadingEdge
## 1: 0.07667469 0.7294118 0.9747650 1 11168
## 2: 0.05142649 -0.5800000 -0.7648210 1 5684
## 3: 0.04406403 0.3640985 0.5992833 3 23,5684
## 4: 0.04595381 0.3801111 0.6793965 4 5577,5566,5573
## 5: 0.35248786 -0.8081591 -1.6458118 5 84335,2932,572
## 6: 0.07436254 -0.7400000 -0.9758061 1 207#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.1 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.0.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.5
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.39
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.21.1 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.0.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()